In a typical wireless communication network, wireless devices, also known as mobile stations and/or user equipments (UEs), communicate via a Radio Access Network (RAN) to one or more core networks. The RAN covers a geographical area which is divided into cell areas, with each cell area being served by a base station, e.g., a radio base station (RBS), which in some networks may also be called, for example, a “NodeB” or “eNodeB” (eNB). A cell is a geographical area where radio coverage is provided by the radio base station at a base station site or an antenna site in case the antenna and the radio base station are not collocated. Each cell is identified by an identity within the local radio area, which is broadcast in the cell. Another identity identifying the cell uniquely in the whole wireless communication network is also broadcasted in the cell. One base station may have one or more cells. The base stations communicate over the air interface operating on radio frequencies with the wireless devices within range of the base stations.
A Universal Mobile Telecommunications System (UMTS) is a third generation mobile communication system, which evolved from the second generation (2G) Global System for Mobile Communications (GSM). The UMTS terrestrial radio access network (UTRAN) is essentially a RAN using wideband code division multiple access (WCDMA) and/or High Speed Packet Access (HSPA) for wireless devices. In a forum known as the Third Generation Partnership Project (3GPP), telecommunications suppliers propose and agree upon standards for third generation networks and UTRAN specifically, and investigate enhanced data rate and radio capacity. In some versions of the RAN as e.g. in UMTS, several base stations may be connected, e.g., by landlines or microwave, to a controller node, such as a radio network controller (RNC) or a base station controller (BSC), which supervises and coordinates various activities of the plural base stations connected thereto. The RNCs are typically connected to one or more core networks.
Specifications for the Evolved Packet System (EPS) have been completed within the 3rd Generation Partnership Project (3GPP) and this work continues in the coming 3GPP releases. The EPS comprises the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), also known as the Long Term Evolution (LTE) radio access, and the Evolved Packet Core (EPC), also known as System Architecture Evolution (SAE) core network. E-UTRAN/LTE is a variant of a 3GPP radio access technology wherein the radio base stations are directly connected to the EPC core network rather than to RNCs. In general, in E-UTRAN/LTE the functions of a RNC are distributed between the radio base stations, referred to as eNodeBs (eNB) in LTE, and the core network. As such, the RAN of an EPS has an essentially “flat” architecture comprising radio base stations without reporting to RNCs.
Cellular communication networks evolve towards higher data rates, together with improved capacity and coverage. In the 3rd Generation Partnership Project (3GPP) standardization body technologies like GSM, HSPA and LTE have been and are currently developed.
LTE is the latest technology standardized. It uses an access technology based on Orthogonal Frequency Division Multiplexing (OFDM) for the downlink (DL) and Single Carrier Frequency Division Multiple Access (SC-FDMA) for the uplink (UL). The resource allocation to wireless devices on both DL and UL is performed adaptively by the concept of fast scheduling, taking into account the instantaneous traffic pattern and radio propagation characteristics of each wireless device. Assigning resources in both DL and UL is performed in the scheduler situated in the eNB.
In LTE all packets are delivered using the Internet Protocol (IP) protocol. This means that also traditionally circuit switched services such as voice conversation will make use of fast scheduling and is called Voice over IP (VoIP). In a typical VoIP arrangement a voice encoder on a transmitter side encodes the speech into packets. When the transmitter is in Silence Insertion Descriptor (SID) state the encoder will generate packets once every 160 ms (6.25 Hz) and in TALK state packets will be generated once every 20 ms (50 Hz). Voice over LTE (VoLTE) enables LTE networks to provide voice services. To improve battery performance VoLTE wireless devices are configured to use Discontinuous Reception (DRX) where the wireless device only need to wake up to listen for scheduling decisions with a periodicity of e.g. 40 ms (25 Hz).
Additionally, in some situations, when a channel quality is less than perfect, a scheduler will perform segmentation and Hybrid Automatic Repeat Request (HARQ) retransmissions. Segmentation is used when the VoLTE packet cannot be transmitted in one subframe, which could be due to e.g. high pathloss or interference. A packet is then segmented into two or more segments which are transmitted in subsequent Transmission Time Intervals (TTIs), i.e. with a periodicity of 1 ms (1000 Hz). HARQ retransmissions are used when packets are decoded incorrectly. In the LTE uplink, synchronous HARQ is used. Hence the time between initial transmission and retransmission will be 8 ms (125 Hz), which is the normal HARQ Round-Trip Time (RTT).
Link adaptation is performed for matching of the modulation, coding and other signals and protocol parameters to the conditions on a channel, also referred to as a radio channel. In order to perform link adaptation in support of the transmission of VoLTE traffic in the uplink, it is necessary to have access to channel estimates. Such estimates are tied to the transmission of the VoLTE packets and are available when a VoLTE packet is sent in the uplink. This means that when a scheduling decision is to be made for the next transmission, the channel estimate is subject to a delay, i.e. the channel may have changed since the last measurement was made. The measurement rates are summarized in the table I below.
TABLE IMeasurement rates of different statesStateMeasurement rateSilence Insertion6.25 Hz  Descriptor (SID)TALK, no bundling of50 HzpacketsTALK, bundling of 225 HzpacketsOccasional125 Hz retransmissionOccasional1000 Hz segmentationVoLTE Scheduling and Link Adaptation
FIG. 1 shows a schematic picture describing the measurement reception and scheduling phases of a typical VoLTE scheduling and link adaptation method
Channel measurement reception is performed in the following way.
Action 1. An uplink transmission is received by the eNodeB.
Action 2. This enables the eNodeB to estimate the received channel power at the eNodeB and normalize it with the transmit power of the wireless device. A channel gain can be estimated with a resolution of one channel matrix per Physical Resource Block (PRB) or better, but due to the relatively little data in a VoLTE packet, VoLTE transmissions tend to be narrow band and it is usually enough to use an average over the whole allocation for link adaptation.
Action 3. The channel gain samples are then filtered to smooth out measurement noise. One filter state per wireless device is maintained and used when the wireless device is scheduled.
Uplink scheduling is shown on the right side of FIG. 1.
This section describes the scheduling method for a single wireless device. Inputs are a buffer size of the wireless device, channel gain and interference. The output is the selected Transport Block Size (TBS), modulation and coding scheme (MCS), and an allocation size for the scheduled UE.
Action 11. The channel gain for the wireless device, updated in step 3 above, is fetched from the channel gain filter.
Action 12. The allocation size parameter is initialized to 1 PRB.
Action 13. The transmit power is estimated. The Transmit Power Spectral Density (PSDtx) for the given allocation size is calculated. This is done based on a latest power headroom report, channel gain and allocation size.
Action 14. A Signal to Interference plus Noise Ratio (SINR) is calculated as based on the PSDtx, channel gain and noise and interference according toSINR=PSDtx+gain−interferencewhere all quantities are given in dB and the interference is measured by the eNodeB.
Action 15. The transport block size (TBS) and modulation and coding scheme (MCS) are calculated from the SINR based on a table lookup. This table is designed to give the TBS that gives 10% Block Error Rate (BLER) for a given SINR.
Action 16. If the TBS is larger or equal to the packet size, i.e. the estimated amount of data in the buffer of the wireless device, the TBS, MCS and allocation size is stored and the loop is done. If not, the allocation size is increased and another iteration in the loop is started at action 13.
The Doppler Spectrum
This disclosure concerns prediction of a correlated signal e.g. a signal representing the uplink VoLTE signal. To understand the problem a review of the fading properties of the LTE channel is needed. There are two effects that need to be understood, these are the fading due to delay spread and the fading due to the Doppler effect. As examples are focused on LTE uplink, it is assumed here that the transmitter is located in the wireless device and the receiver in the base station, i.e. the eNB. However, the transmitter and the receiver may e.g. be located in a respective wireless device communicating with one another.
The delay spread is a parameter related to the Power Delay Profile (PDP) of the signal energy seen by the receiver. When a radio pulse or signal is transmitted from a transmit antenna of the transmitter, the radio signal travels to the receiver along different paths, where it experiences different reflections and different scattering for each path. Each path is hence associated with a different distance from the transmitter to the receiver. Since the speed of light is constant, the signal energy arrives at different times in the receiver, although it is sent out at one single point in time. The result is so called delay spread, depicted in FIG. 2. FIG. 2 shows a power delay profile of a radio signal.
Normally this multipath effect is modelled by a finite impulse response filter discretized on the delay. Since the delay spread may be of the order if 1 micro second, the corresponding frequency variation is in the MHz region. When expressed in the frequency domain, such fading is denoted frequency selective fading.
Now the wireless device is sometimes moving. This means that the radio waves transmitted from the wireless device appear compressed in the receiver of the eNB when the wireless device moves towards the receiver, and decompressed when the wireless device moves in the opposite direction i.e. from the receiver. This Doppler effect hence results in a frequency shift with a size roughly equal to the distance traveled per second divided by the wavelength of the carrier frequency, i.e. by
                              f          D                =                                            v              ⁢                                                          ⁢              Δ              ⁢                                                          ⁢                              t                /                λ                                                    Δ              ⁢                                                          ⁢              t                                =                                    v              λ                        =                                          v                c                            ⁢                              f                c                                                                        (                  eq          .                                          ⁢          1                )            where fD is the Doppler frequency, v the wireless device speed, c the speed of light, λ is the wavelength, and fc is the carrier frequency.
For 700 MHz and a UE speed of 3 kmph, the Doppler frequency is therefore about 2 Hz. It is hence a much slower process than the frequency selective fading process. Embodiments herein relate to prediction of the power variations due to the Doppler effect,
The LTE uplink channel is represented on a time/frequency grid. That grid is normally 20 MHz wide, while the time resolution is 1 ms. For that reason frequency selective fading appears as a power variation, for fixed time over the 20 MHz frequency range, while the Doppler fading is seen as a slow variation over time. The LTE uplink channel, subject to both fading effects appears in FIG. 3.
To be able to predict the power variations of such a channel in the best way, a model of the channel is needed. Some further discussion of the representation of Doppler fading is therefore needed.
It is the case that the energy components that build up the PDP of FIG. 3 travel different distances, they therefore also arrive at the receiver from different directions. As a result, the wireless device movement will not be represented by a single Doppler frequency but rather a distribution of frequencies. Several models have been developed in the communications literature, based on different assumptions on the angle of arrival distribution. Common to most of these modes is that the Doppler power spectrum can be represented by a linear filter, with a bandwidth roughly equal to the Doppler frequency of (eq. 1). The filter is hence of a lowpass type. Further, since the models are statistical ones, and since the filter represents a power spectrum, it follows from standard statistical theory for stochastic processes that the Doppler spectrum can be represented by a linear filter, with a white noise input, i.e. assD(t)=H(q−1)e(t)  (eq. 2)where q−1 denotes a delay operator, i.e. q−1e(t)=e(t−T) where T is the sampling period. sD(t) is the Doppler spectrum output and e(t) is noise.Optimal Prediction
To predict the value of the linear filter or model of (eq. 2) ahead in time it is suitable to exploit the theory of optimal prediction. Two main approaches exist, based either on the linear filter of (eq. 2) or by a state space representation of it. The two methods give identical results, but exploit slightly different embodiments. The first method based on the linear filter will be discussed in detail, with the second state space based method being commented on briefly. However, it is obvious for anyone skilled in the art that the principles and techniques disclosed in embodiments herein are applicable to any of the methods, hence the validity of the application should cover also these methods.
The linear filter or model of (eq. 2) allows prediction schemes to be developed. In order to obtain a linear optimal predictor the linear filter or model of (eq. 2) is first specified to be an all-pole model of the form:
                                          s            D                    ⁡                      (            t            )                          =                              1                          A              ⁡                              (                                  q                                      -                    1                                                  )                                              ⁢                      e            ⁡                          (              t              )                                                          (                  eq          .                                          ⁢          3                )                                          A          ⁡                      (                          q                              -                1                                      )                          =                  1          +                                    a              1                        ⁢                          q                              -                1                                              +          …          +                                    a              n                        ⁢                          q                              -                n                                                                        (                  eq          .                                          ⁢          4                )            
Multiplication from the right with A(q−1) then leads to the equationsD(t)=a1sD(t−T)+ . . . +ansD(t−nT)+e(t)  (eq. 5)
Now consider prediction of sD(t) given measurements up to time t−T. Since the optimal prediction of a white zero mean noise sample equals 0, it follows that the optimal predictor isŝD(t)=a1sD(t−T′)+ . . . +ansD(t−nT)=θTφ(t)  (eq. 6)θ=(a1 . . . an)T  (eq. 7)φ(t)=(sD(t−T) . . . sD(t−nT))T  (eq. 8)
Hence, by measuring the Doppler related outputs, the value at the next sampling time instance can be predicted from old values. This requires that the filter is known. This issue is treated in the next subsection. The T means vector and matrix transpose, i.e. turning rows into columns and vice versa.
The second possible prediction method, i.e. the state space approach, starts by a formulation of the filter model (eq. 2) in state space form. This puts the model in a framework were the classical techniques of Kalman filtering can be applied. The details of this are not reproduced here since it can be found in numerous textbooks on optimal filtering.
Two characterizing aspects of the above methods need to be highlighted before proceeding.
i) The filter of the filter model H(q−1) does not depend on time.
ii) The sampling rates of the models are constant.
Recursive Model Estimation
Now, the filter model is not perfectly constant in the VoLTE channel case, it rather varies with the speed of the wireless device and with the environment encountered by the contributing radio signals. It is therefore necessary to estimate the model on-line, with recursive methods. Also this is a technique that is well known in prior art. Many algorithms are available, also in this case either in filter form or state space form.
As will be seen the optimal prediction discussed above is an integrated part of the following standard recursive least squares estimator.
                                          P            ⁡                          (              t              )                                =                                    (                                                P                  ⁡                                      (                                          t                      -                      T                                        )                                                  -                                                      P                    ⁡                                          (                                              t                        -                        T                                            )                                                        ⁢                                      φ                    ⁡                                          (                      t                      )                                                        ⁢                                                            φ                      T                                        ⁡                                          (                      t                      )                                                        ⁢                                      P                    ⁡                                          (                                              t                        -                        T                                            )                                                                                  )                                      λ              ⁡                              (                                  λ                  +                                                                                    φ                        T                                            ⁡                                              (                        t                        )                                                              ⁢                                          P                      ⁡                                              (                                                  t                          -                          T                                                )                                                              ⁢                                          φ                      ⁡                                              (                        t                        )                                                                                            )                                                    ⁢                                  ⁢                              K            ⁡                          (              t              )                                =                                                    P                ⁡                                  (                  t                  )                                            ⁢                              φ                ⁡                                  (                  t                  )                                                                    λ              +                                                                    φ                    T                                    ⁡                                      (                    t                    )                                                  ⁢                                  P                  ⁡                                      (                    t                    )                                                  ⁢                                  φ                  ⁡                                      (                    t                    )                                                                                      ⁢                                  ⁢                                                            s                ^                            D                        ⁡                          (              t              )                                =                                                    φ                T                            ⁡                              (                t                )                                      ⁢                                          θ                ^                            ⁡                              (                                  t                  -                  T                                )                                                    ⁢                                  ⁢                                            θ              ^                        ⁡                          (              t              )                                =                                                    θ                ^                            ⁡                              (                                  t                  -                  T                                )                                      +                                          K                ⁡                                  (                  t                  )                                            ⁢                              (                                                                            s                      D                                        ⁡                                          (                      t                      )                                                        -                                                                                    s                        ^                                            D                                        ⁡                                          (                      t                      )                                                                      )                                                                        (                  eq          .                                          ⁢          9                )            
It can again be seen that the sampling period is constant. In (eq. 9) P(t) is the estimated covariance matrix and K(t) is the update gain vector.
Problems with Existing Solutions
To get an efficient VoLTE solution in terms of both spectrum efficiency and battery efficiency it is important to keep the transmissions from the transmitter of the wireless device to a minimum since uplink transmissions create interference and consume precious battery wireless device. Therefore, the Sounding Reference Symbols (SRS) which is commonly used for channel quality estimation for best effort type of traffic is not a good design option.
Also, to save battery of the wireless device the DRX period needs to be kept as long as possible, maybe as long as 40 ms. This means that the only uplink transmissions that can be used for channel quality estimation are with a period of ˜40 ms. Hence, for common speeds of wireless devices when a user is walking, the channel quality estimate can be outdated before it is used for scheduling and link adaptation of the next packet.
FIG. 4 shows the SINR estimation error for three different methods for channel quality estimation. In the “ideal” method the channel quality is available in the eNodeB in every subframe. However, since there is a delay between the scheduling and link adaptation for a VoLTE packet until the wireless device transmits the data, typically 4-5 ms for LTE Frequency Division Duplex (FDD), the channel will have time to change causing an error in the estimated channel quality.
A method of using the latest channel quality estimate is denoted the Zero-Order Hold (ZOH). This method is simple and works very well for slow moving wireless devices. However, as the speed of the wireless device increases the channel quality estimates will get more and more outdated when subsequent VoLTE packets are scheduled. At some point the channel will change almost completely between VoLTE packets, and then the performance of the ZOH method will be poor.
For fast wireless devices, i.e. for wireless devices for which the channel changes a lot between VoLTE packets the advantage of trying to use instantaneous channel knowledge is very limited. In that case the “average” method is a more sensible method. This method will instead use a long-term filtered channel quality and hence effectively reducing the maximum channel quality estimation error compared to the ZOH method.
FIG. 4 shows an accuracy of the SINR estimations for a typical VoLTE scenario. The pathloss is 120 dB and the wireless device speed is 7 km/h. Channel estimation using ZOH is a curve marked with one line and channel estimation using averaged channel gain is a curve marked with two lines.
Both under-estimation and over-estimation of the channel quality will lead to less efficient use of the channel. When the channel is over-estimated this will cause packets to be received incorrectly, e.g. Cyclic Redundancy Check (CRC) check fails. This will in turn require a retransmission of the same packet, and this retransmission also consumes channel resources. If, on the other hand the channel is under-estimated, this will lead to a use of too low MCS. And since a lower MCS means that fewer bits can be transmitted per PRB more PRBs has to be used to transmit the same packet. If these PRBs are not available the packet has to be segmented and transmitted in two separate, and maybe consecutive, TTIs.
As stated above, two constraints are valid for the optimal predictor and the recursive estimation algorithm to be valid. These require that the sampling period is constant and that the filter is also constant. However, this is not the case for the VoLTE channel, where channel measurements can occur with at least five different rates depending on the circumstances. This has substantial drawbacks, among these                i) In order for the optimal predictor and estimator to be able to handle multiple sampling rates, the multiple sampling rates need to be run with the fastest rate. This means that the computational complexity per instance will be maximal, even when slower rate measurements are to be processed.        ii) In case of slower sampling rates, the missing measurements need to be replaced by something else. Even with such measurement replacements the obtained result won't be optimal.        iii) Fast sampling rates means that the poles of the estimated all-pole model approaches 1. This is known to cause numerical problems and numerical inaccuracy.        iv) As an alternative, one channel estimator could be used for each sampling rate, however that would also need to an increased complexity, without solving all problems above. In addition, the estimated model of the channel will be different for different sampling rates, hence it is unclear how to merge different models to enhance the link adaptation performance.        
Thus, the performance of the wireless communication network may be reduced when using present techniques.